1. Field of Invention
The present invention relates to a face detection method, and more particularly to a face detection method for detecting a face in a picture under detection at different transposed positions.
2. Related Art
In recent years, human facial recognition systems have paid great attention from research scholars and the industry and have been deeply expected to show excellent performance on public security or door-forbidden systems. However, such kinds of systems are always influenced by external factors such as light rays or complex textures, and thus reducing a success rate of recognition.
In order to solve the above influences of the external factors, it is suggested to use different image features to effectively detect a face in a picture under detection. In general, the most common face detection method utilizes a learning model to memorize multiple pictures to be detected. The learning model learns to recognize if the pictures under detection contain the preset image features according to the preset image features. Both the active learning architecture, for example, a neural network, an expert system, a fuzzy system, and the classified learning architecture, for example, a support vector machine (SVM), a principal components analysis (PCA), a SNoW method, a boosting method need to perform learning behaviors according to the set image features. Therefore, how to create the learning model and select proper image features are crucial for the face detection determination.
In order to distinguish a face from a background in a picture under detection, a Haar-like algorithm is often utilized to retrieve facial features. The Haar-like algorithm is a method for performing a feature processing on a textural directionality of patterns. Therefore, the Haar-like algorithm can effectively distinguish the face from the complex background. Also because the Haar-like algorithm depends on the textural directionality in the picture under detection, when the picture under detection is transposed to different directions, e.g., transposed by 90, 180 or 270 degrees, the original training samples obtained by the Haar-like algorithm would not be applicable to the transposed picture under detection.
In order to detect the face in the picture under detection at different transposed positions, the Haar-like algorithm is utilized again to perform a learning training on the picture under detection at different transposed positions repeatedly. In this manner, the memory space as well as the operation time is greatly increased.
Additionally, in order to determine a size of the face, an ellipse mask selection method is generally used to determine a size of area of the face in the picture under detection. In an edge image with good inspected quality, contours of the face and head portions can be regarded to be approximately elliptical shaped. Referring to FIGS. 1a and 1b, since the conventional ellipse mask has a fixed major to minor axis ratio, a problem that a circled area is incomplete will occur no matter a large ellipse mask or a small ellipse mask is used. If the face 120 in the picture under detection 100 is larger, a larger ellipse mask 110 is used to select the face 120. Similarly, if the face 120 in the picture under detection 100 is smaller, a smaller ellipse mask 110 is used to select the face 120.